Data reconstruction is an important issue in a variety of applications. For example, such as in medical imaging applications, raw data is often acquired over a three-dimensional (3D) anatomical space. Using magnetic resonance (MR) imaging, in particular, 3D data is often acquired as a sequence of 2D images obtained as contiguous slices. The 3D image is reconstructed by concatenating the contiguous 2D slice images.
Often, the resolution of the data in one or more dimensions of the 3D data set is not comparable to the resolution in the remaining dimensions. For example, in medical imaging, such as in MR imaging, the in-plane resolution of the 2D MR image may be much finer than the slice resolution. In other words, the pixel dimensions within the MR image plane may be much finer than the slice thickness that extends normal to the MR image plane. For simplicity, the dimension having a coarser resolution is referred to herein as the “deficient dimension,” while the other dimensions are referred to herein as “non-deficient dimensions.”
Given the mismatch in resolution, it is desirable to increase the resolution in the deficient dimension to match the resolution in the non-deficient dimensions. Thus, various interpolation techniques have been developed to enhance the resolution in the deficient dimension.
Unfortunately, there is still much room for improvement in known interpolation techniques.